Popularity

8.8

Stable

Activity

9.8

Stars 6,146

Watchers 193

Forks 929

Last Commit 1 day ago

Description

CatBoost is a machine learning method based on gradient boosting over decision trees.

Main advantages of CatBoost: * Categorical features support * Reduced overfitting * User-friendly API interface * Fast model inference

Programming language: C

License: Apache License 2.0

Tags: Machine Learning Gradient Boosted Decision Trees CUDA

Latest version: v0.24.3

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* Code Quality Rankings and insights are calculated and provided by Lumnify.
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README

Website | Documentation | Tutorials | Installation | Release Notes

CatBoost is a machine learning method based on gradient boosting over decision trees.

Main advantages of CatBoost:

Superior quality when compared with other GBDT libraries on many datasets.
Best in class prediction speed.
Support for both numerical and categorical features.
Fast GPU and multi-GPU support for training out of the box.
Visualization tools included.

Get Started and Documentation

All CatBoost documentation is available here.

Install CatBoost by following the guide for the

Next you may want to investigate:

If you cannot open documentation in your browser try adding yastatic.net and yastat.net to the list of allowed domains in your privacy badger.

Catboost models in production

If you want to evaluate Catboost model in your application read model api documentation.

Questions and bug reports

For reporting bugs please use the catboost/bugreport page.
Ask a question on Stack Overflow with the catboost tag, we monitor this for new questions.
Seek prompt advice at Telegram group or Russian-speaking Telegram chat

Help to Make CatBoost Better

Check out open problems and help wanted issues to see what can be improved, or open an issue if you want something.
Add your stories and experience to [Awesome CatBoost](AWESOME.md).
To contribute to CatBoost you need to first read CLA text and add to your pull request, that you agree to the terms of the CLA. More information can be found in CONTRIBUTING.md
Instructions for contributors can be found here.

News

Reference Paper

Anna Veronika Dorogush, Andrey Gulin, Gleb Gusev, Nikita Kazeev, Liudmila Ostroumova Prokhorenkova, Aleksandr Vorobev "Fighting biases with dynamic boosting". arXiv:1706.09516, 2017.

Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin "CatBoost: gradient boosting with categorical features support". Workshop on ML Systems at NIPS 2017.

License

*Note that all licence references and agreements mentioned in the catboost README section above are relevant to that project's source code only.

catboost

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.